A new approach using the Pierce two-node model for different body parts.

Area of Science:

• Environmental Science
• Human Physiology
• Thermal Comfort

Background:

• Accurate prediction of local skin temperature is crucial for assessing thermal comfort and designing healthy indoor environments.
• Existing models often lack precision in accounting for regional variations in skin temperature across the human body.

Purpose of the Study:

• To present a novel application of the Pierce two-node model for predicting local skin temperatures with high accuracy.
• To adapt the model for individual body parts by adjusting set-points and modifying the calculation algorithm.

Main Methods:

• Collected local skin temperature data from 11 subjects at 24 sites under neutral, warm, and cold indoor conditions.
• Utilized neutral condition data to calibrate local skin set-points for each body part.
• Employed a line search method to determine local core set-points, optimizing model prediction accuracy.

Main Results:

• The modified Pierce two-node model achieved a maximum deviation of ±0.1°C for predicting skin temperatures under neutral conditions.
• Validation against warm and cold conditions demonstrated an average prediction deviation of ±0.3°C.
• The model successfully predicted local skin temperatures across different body parts and thermal environments.

Conclusions:

• The adapted Pierce two-node model provides a highly accurate method for predicting local skin temperatures.
• This approach enhances the understanding of regional thermal responses and improves thermal comfort assessments.
• The modified model offers a valuable tool for research in environmental science and human physiology.